Inferring causal molecular networks: empirical assessment through a community-based effor

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  • dc.contributor.author Hill, Steven M.ca
  • dc.contributor.author Anton, Bernatca
  • dc.contributor.author Bonet Martínez, Jaume, 1982-ca
  • dc.contributor.author García-García, Javier, 1982-ca
  • dc.contributor.author Oliva Miguel, Baldomeroca
  • dc.contributor.author Planas Iglesias, Joan, 1980-ca
  • dc.contributor.author Poglayen, Daniel, 1984-ca
  • dc.contributor.author Mukherjee, Sachca
  • dc.contributor.author The HPN-DREAM Consortiumca
  • dc.date.accessioned 2016-05-26T16:26:50Z
  • dc.date.available 2016-05-26T16:26:50Z
  • dc.date.issued 2016
  • dc.description.abstract It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.ca
  • dc.description.sponsorship This work was supported in part by the US National Institutes of Health (National Cancer Institute (NCI) grants U54 CA 112970 (to J.W.G.) and 5R01CA180778 (to J.M.S.), NCI award U54CA143869 to M.F.C. and National Institute of General Medical Sciences award 1R01GM109031 to J.M.S.), the Susan G. Komen Foundation (SAC110012 to J.W.G.), the Prospect Creek Foundation (grant to J.W.G.), the EuroinvesXgacion program of MICINN (Spanish Ministry of Science and InnovaXon), partners of the ERASysBio+ iniXaXve supported under the EU ERA-NET Plus Scheme in FP7 (SHIPREC), MICINN (FEDER BIO2008-0205, FEDER BIO2011-22568 and EUI2009-04018 to B.O.), the Royal Society (Wolfson Research Merit Award to S.M.), the German Federal Ministry of Education and Research GANI_MED Consortium (grant 03IS2061A to T.K.), and the US National Library of Medicine (grants R00LM010822 (to X.J.) and R01LM011663 (to X.J. and R.E.N.))
  • dc.format.mimetype application/pdfca
  • dc.identifier.citation Hill SM, Heiser LM, Cokelaer T, Unger M, Nesser NK, Carlin DE et al. Inferring causal molecular networks: empirical assessment through a community-based effor. Nature methods. 2016; 13(4): 310-318. DOI 10.1038/nmeth.3773-0ca
  • dc.identifier.doi http://dx.doi.org/10.1038/nmeth.3773
  • dc.identifier.issn 1548-7091
  • dc.identifier.uri http://hdl.handle.net/10230/26763
  • dc.language.iso engca
  • dc.publisher Nature Publishing Groupca
  • dc.relation.ispartof Nature methods. 2016; 13(4): 310-318
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/BIO2011-22568
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/EUI2009-04018
  • dc.rights This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.ca
  • dc.rights.accessRights info:eu-repo/semantics/openAccessca
  • dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/ca
  • dc.subject.other Molèculesca
  • dc.title Inferring causal molecular networks: empirical assessment through a community-based efforca
  • dc.type info:eu-repo/semantics/articleca
  • dc.type.version info:eu-repo/semantics/publishedVersionca

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Articles (Departament de Medicina i Ciències de la Vida)
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